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Trajectory-based Human Action Recognition

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  • Wed, 05/16/2018 - 1:00pm - 3:00pm




Trajectory-based Human Action Recognition

Doctoral Dissertation by

Pejman Habashi

Date: Wednesday, May 16th, 2018
Time: 1:00 pm – 3:00 pm
Location: Erie Hall, G141

Abstract: Human activity recognition is an important and active research area in computer vision that has numerous challenges. Although trajectories have been used for human action recognition by many researchers, they have not been investigated thoroughly, and their full potential has not been put to the test before this work. 

This thesis formally defines three different trajectory extraction methods, namely interest-point based trajectoriesLucas-Kanade based trajectories, and Farnback optical flow based trajectories. Their discriminant power for human activity recognition task is evaluated and a better trajectory shape descriptor is also proposed, which is a superset of the existing descriptors in the literature. Finally, a method is proposed to augment the trajectories with disparity information. The latter is relatively easy to extract from a pair of stereo images, and it is capable of capturing the 3D structure of the scene. To the best of our knowledge, this work is the first to use the disparity information with trajectories for human activity recognition. The proposed disparity-augmented trajectories have improved the discriminant power of traditional dense trajectories by a significant 3.11%.

Thesis Committee:    
Internal Readers:   Dr. Dan Wu and Dr. Jianguo Lu
External Program Reader: Dr. Kemal Tepe
External Examiner: Dr. Faisal Qureshi
Advisor:               Dr. Boubakeur Boufama
Co-Advisor:         Dr. Imran Ahmad
Chair:                   Dr. Hyuk-Jae (Jay) Rhee



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